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研究海冰的声学探测技术需要实际海冰和低温环境。介绍了设计制造模拟海水结冰的试验装置的方法。采用人工制冷技术在实验室环境下实现了封闭容器里海水表面冻结一定厚度的海冰。试验结果表明，将容器中接近冰点的海水持续冻结32h后，海冰的厚度达到20cm。对容器里的海冰进行了声波信号探测冰水界面的试验，为测冰声呐技术研究和测冰试验创造了有利条件。 相似文献
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夏季加拿大海盆海冰边缘区声体积后向散射强度研究 总被引：1，自引：1，他引：0
声信号的体积后向散射强度是声传播过程中一个关键的参数。海冰边缘区的声体积后向散射强度研究对深入认识北极声场环境有着十分重要的意义。本文利用中国第六次北极科学考察获取的数据资料研究了海冰边缘区声体积后向散射强度特性。结果表明:加拿大海盆海冰边缘区是声体积后向散射强度的明显过渡区。无冰海面(海冰密集度小于15%)海洋深层水的声体积后向散射强度明显大于密集海冰区域的海水(海冰密集度大于50%)。讨论了声体积后向散射强度与海冰融化之间的关系,造成融冰区声体积后向散射强度增大的原因是水下悬浮泥沙、浮游生物等悬浮物质增加。根据海冰密集海域的海水后向散射强度弱的特点,对北极下放式声学多普勒测流仪(LADCP)观测的设置提出建议。 相似文献
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基于CryoSat-2卫星测高数据的北极海冰体积估算方法 总被引：1，自引：1，他引：0
近30年来,北极海冰正发生着剧烈的变化。海冰体积是量化海冰变化的重要指标之一。本文以2015年CryoSat-2卫星测高数据和OSI SAF海冰类型产品为基础。提取了浮冰出水高度、积雪深度、海冰密集度、海冰类型等属性信息,通过数据内插、投影变换、栅格转换、空间重采样等工作将海冰属性信息统一为25 km×25 km分辨率的栅格数据集。根据流体静力学平衡原理,逐个估算栅格像元对应的海冰厚度值,将其与对应的海冰面积相乘,估算了北极海冰密集度大于75%海域的海冰体积,并分析了海冰厚度和体积的月变化和季节变化特征。用NASA IceBridge海冰厚度产品对反演的海冰厚度进行验证。结果表明二者相关系数为0.72,有较高的一致性。北极海冰平均厚度春季最大,夏季最小,分别约为2.99 m和1.77 m,最厚的海冰集中在格陵兰沿岸北部和埃尔斯米尔半岛以北海域。多年冰平均厚度大于一年冰。冬季海冰体积最大,约为23.30×103 km3,经过夏季的融化,减少了近70%。一年冰体积季节波动较大,而多年冰体积相对稳定,季节变化不明显。 相似文献
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在全球变暖背景下,北极海冰覆盖面积持续减少,对全球的温盐环流、海洋生物化学过程和气候变化产生了深远的影响,研究北极古海冰的变化可以使我们对北极环境有全面的认识,更准确地预测其未来的变化规律。近十年来,一种新发展的海冰生物标志物IP_(25)(Ice Proxy with 25 carbon atoms)被广泛用于北极及亚北极的海冰重建。IP_(25)是北极冰藻产生的一种高度分化的单不饱和类异戊二烯(HBIs),能够稳定地保存在海洋沉积物中。自从IP_(25)被发现以来,越来越多的研究者对其指示海冰变化的应用进行了深入的研究。本文首先总结了重建古海冰的传统指标和限制性并介绍了IP_(25)指示海冰的原理、由定性到定量的发展以及存在的局限性。然后归纳了利用IP_(25)重建北极地区海冰分布和变化的实例研究,涵盖了北冰洋中心、陆架边缘海、河口以及亚北极地区不同空间海域,跨越了近现代、全新世、第四纪以及中新世不同时间尺度。其中,近现代的海冰重建结果与海冰的卫星观测数据取得了很好的相关性,为古海冰的重建提供了基础;古海冰的重建为数值模拟古气候以及预测未来海冰变化趋势提供了重要依据。 相似文献
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国家海洋环境预报中心承担的“七五”科技攻关项目──“76-02-07”海冰数值预报方法研究取得突破性进展,顺利通过专家鉴定和国家验收。 海冰是我国重要的灾害性海况之一,在我国渤海北部,历史上曾出现过严重的海上冰封,致使平台被冰推翻,拉筋被割断,既使素有不冻港之称的秦皇岛由于冰封长达月余之久,从天津港口进出的一百多艘货船被冰阻挡无法航行,经济损失惊人。由此可见,随着海事活动的日益频繁,海冰已成为海上生产作业和航行不可轻视的天敌。从1958年开始,用30多年的时间、逐步建了立适合我国国情的海冰观测,调查和预报系统,但这种经验加统计的定性趋势预报方法,远不适应当前经济建设的发展,给海上施工、生产、交通、航运带来严重的影响。1986年以吴辉碇副教授为首的海冰专题科技攻关研究组,克服海冰观测资料奇缺,预报系统不健全的种种困难,经过5年的艰苦攻关,以我国海冰前期预报和科研为基础,博国际数十年海冰预报和研究之经验,采美国、芬兰、瑞典和苏联等国的先进的海冰模式,结合渤海的气候,水文和冰情特点,就海冰的生、消、增长漂移及海冰本身的许多物理特性作了大量理论和数值试验,合理地考虑海冰动力-热力特性、海冰大气、海冰与海洋的相互作用,采用塑性-粘性结构原理计算冰内力。推出了适 相似文献
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巴伦支海和喀拉海区域是北极海冰变化最显著的地区之一,由于该区域海冰厚度和海冰流速观测数据的不完整,对于巴伦支海和喀拉海区域海冰体积时空变化特征的认识还不够完善,对引起该区域海冰变化的关键过程及海冰对热力学因素的响应理解也不够清晰。本研究综合利用1991—2019年海冰流速和海冰厚度再分析数据与海冰流速模式数据,定量分析了巴伦支海和喀拉海区域海冰变化的主要贡献来源。结果表明,相较于外部的海冰输运变化,局地海冰变化贡献是该区域海冰总体积变化的关键过程。外部海冰的贡献主要由斯瓦尔巴群岛与北地群岛之间的海冰向外输出增加和内向输入减少共同作用引起。在局地海冰变化中,海冰体积减小主要由喀拉海和巴伦支海北部以及喀拉海南部海冰厚度减小所致。区域内海冰分别和海表温度以及地表气温呈现较为显著的负相关性变化。海冰与海表温度高相关性区域的分布与海表温度增暖较快的区域相一致,春、夏季主要位于巴伦支海,秋、冬季扩大至巴伦支海和喀拉海大部分区域。海冰与地表气温之间的平均相关系数高于与海表温度的相关系数,高相关性区域的分布与海冰厚度减小明显的区域分布相似。 相似文献
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受全球气候变化的影响,极区海浪尤其是北极海浪在过去几十年发生了显著的变化,使得海冰边缘区海冰与海浪的相互作用愈发显著。本文从物理海洋学的角度出发,较系统地总结了海冰对海浪作用研究的国内外现状,从理论和实测的角度分别探讨了海冰对海浪能量的耗散及其引起的波动频散关系的变化,同时分析了当前海冰覆盖海域海浪的数值模拟与现场观测研究,指出了未来开展有冰海域海浪数值模拟与预报所面临的主要问题,并对该方向今后的研究做出展望。总体来看,尽管海冰对海浪作用的机理复杂且与海冰类型高度相关,但是海冰对海浪能量的衰减与传播距离基本呈指数关系,并且海冰会一定程度上影响海浪的传播速度。未来依然需要更多不同海冰类型下海浪的观测数据以开展进一步的机理分析、模型检验和参数校准,进而实现高精度的业务化预报。 相似文献
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西北航道是指从北大西洋经加拿大北极群岛进入北冰洋,再进入太平洋的航道,是连接大西洋和太平洋的捷径。为了探讨西北航道通航期极端天气条件下强风及海冰对波浪场的影响机制,建立并验证了考虑海冰影响下的西北航道风浪演化模型,并以2012年8月北极气旋登陆期间为例探讨西北航道通航期波浪特性及波能流密度的时空演化及其对风和海冰的响应。研究结果表明,北极夏季海冰大多分布于西北航道以北海域,而风向大部分集中在SSW(南偏西22.5°)至SW(南偏西45°),西北航道海冰的存在并不会引起有效风区的明显减少,也不会引起无冰海域波能流的明显减小(不超过5%)。但是,当风向变为北向风时,无冰海域波能流减小幅度最多高达62%。最后,综合海冰和波浪要素的时空分布,提出了极端天气条件下西北航道通航期的最佳适航路线,为西北航道的夏季安全通航提供了科学依据。 相似文献
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为给寒区海域的波浪能估算提供科学依据,提出一种合理推算冰水共存海域波浪条件及波能流密度的方法,该方法将海冰模型与水动力学模型耦合模拟得到的冰浓度以线性修正函数的方式纳入波浪模型的海面摩阻风速方程中,并基于MCT (model coupling toolkit)耦合器将海冰模型、水动力学模型与波浪模型进行实时耦合。基于该方法模拟了渤海冬季寒潮大风期间的海冰以及波能流密度的演化。模拟结果表明,在2012年2月5~8日寒潮大风期间,结冰区域占到渤海总面积的1/3,约有76%的渤海海域的平均波能流密度受海冰影响减小,其中辽东湾近岸的波能流密度平均受冰影响最多减小了100%,而渤海湾和莱州湾近岸受冰影响最多分别减小了60%和50%。即使是无冰覆盖的老铁山水道,其波能流密度的最大值也受冰影响减少了14%。耦合模拟可以更为准确地对渤海冬季的波能流密度分布进行评估,为波浪能发电厂选址提供依据。 相似文献
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Focusing on physical processes, we aim at constraining the dynamics of argon (Ar), a biogeochemically inert gas, within first year sea ice, using observation data and a one-dimensional halo-thermodynamic sea ice model, including parameterization of gas physics. The incorporation and transport of dissolved Ar within sea ice and its rejection via gas-enriched brine drainage to the ocean, are modeled following fluid transport equations through sea ice. Gas bubbles nucleate within sea ice when Ar is above saturation and when the total partial pressure of all three major atmospheric gases (N2, O2 and Ar) is above the brine hydrostatic pressure. The uplift of gas bubbles due to buoyancy is allowed when the brine network is connected with a brine volume above a given threshold. Ice-atmosphere Ar fluxes are formulated as a diffusive process proportional to the differential partial pressure of Ar between brine inclusions and the atmosphere. Two simulations corresponding to two case studies that took place at Point Barrow (Alaska, 2009) and during an ice-tank experiment (INTERICE IV, Hamburg, Germany, 2009) are presented. Basal entrapment and vertical transport due to brine motion enable a qualitatively sound representation of the vertical profile of the total Ar (i.e. the Ar dissolved in brine inclusions and contained in gas bubbles; TAr). Sensitivity analyses suggest that gas bubble nucleation and rise are of most importance to describe gas dynamics within sea ice. Ice-atmosphere Ar fluxes and the associated parameters do not drastically change the simulated TAr. Ar dynamics are dominated by uptake, transport by brine dynamics and bubble nucleation in winter and early spring; and by an intense and rapid release of gas bubbles to the atmosphere in spring. Important physical processes driving gas dynamics in sea ice are identified, pointing to the need for further field and experimental studies. 相似文献
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南海北部海区上层水体平均声速场的变化 总被引：1，自引：0，他引：1
对所收集到的南海北部海区的温度、盐度和深度历史资料进行了声速的计算、统计与分析,结果表明该海区上层水体的平均声速场有较明显的年际变化:表层声速的平面分布显示出沿岸水与外海水强弱交替变化的特征,声速等值线的走向几乎与岸线平行,等声速线的值自近岸向外海增加,大陆架外缘海区声速的水平梯度较大;下层声速的分布以环流和水体共同作用的形式出现,50m层声速平面分布的趋势除冬季与表层稍为相似外,其余季节与表层有明显的差别,春、夏季节50m层声速自西向东增加,而秋季与其表层分布相反,自近岸向外海减小;声速的垂直分布受海水升温与降温的影响显著,春、夏季表层海水升温,海表声速最大,声速自海表随深度的增加而减小;秋、冬季表层附近水层降温,声速稍偏低,普遍出现正梯度现象,最大声速移至表层以下的水层,这个深度随季节的改变而改变,随海区的不同而不同.该海区的平均声速有年波动现象,表层波幅最大,随深度的增加波幅变小,至100m水层其波动的位相几乎与表层相反. 相似文献
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An Antarctic sea ice identification algorithm on the HY-2A scatterometer(HSCAT) employs backscattering coefficient(σ0) and active polarization ratio(APR) for a preliminary sea ice identification.Then standard deviation(STD) filtering and space filtering are carried out.Finally,it is used to identify sea ice.A process uses a σ0,STD threshold and an APR as sea ice indicators.The sea ice identification results are verified using the sea ice distribution data of the ASMR2 released by the National Snow and Ice Data Center as a reference.The results show very good consistence of sea ice development trends,seasonal changes,area distribution,and sea ice edge distribution of the sea ice identification results obtained by this algorithm relative to the ASMR2 sea ice results.The accuracy of a sea ice coverage is 90.8% versus the ASMR2 sea ice results.This indicates that this algorithm is reliable. 相似文献
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《Ocean Modelling》2002,4(2):137-172
A new sea ice model, GELATO, was developed at Centre National de Recherches Météorologiques (CNRM) and coupled with OPA global ocean model. The sea ice model includes elastic–viscous–plastic rheology, redistribution of ice floes of different thicknesses, and it also takes into account leads, snow cover and snow ice formation. Climatologies of atmospheric surface parameters are used to perform a 20-year global ocean–sea ice simulation, in order to compute surface heat fluxes from diagnosed sea ice or ocean surface temperature. A surface salinity restoring term is applied only to ocean grid cells with no sea ice to avoid significant surface salinity drifts, but no correction of sea surface temperature is introduced. In the Arctic the use of an ocean model substantially improves the representation of sea ice, and particularly of the ice edge in all seasons, as advection of heat and salt can be more accurately accounted for than in the case of, for example, a sea ice–ocean mixed layer model. In contrast, in the Antarctic, a region where ocean convective processes bear a much stronger influence in shaping sea ice characteristics, a better representation of convection and probably of sea ice (for example, of frazil sea ice, brine rejection) would be needed to improve the simulation of the annual cycle of the sea ice cover. The effect of the inclusion of several ice categories in the sea ice model is assessed by running a sensitivity experiment in which only one category of sea ice is considered, along with leads. In the Arctic, such an experiment clearly shows that a multicategory sea ice model better captures the position of the sea ice edge and yields much more realistic sea ice concentrations in most of the region, which is in agreement with results from Bitz et al. [J. Geophys. Res. 106 (C2) (2001) 2441–2463]. 相似文献
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Qi Shu Fangli Qiao Jiping Liu Zhenya Song Zhiqiang Chen Jiechen Zhao Xunqiang Yin Yajuan Song 《海洋学报(英文版)》2021,40(10):65-75
To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model (FIO-ESM) climate forecast system, satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) are assimilated into this system, using the method of localized error subspace transform ensemble Kalman ?lter (LESTKF). Five-year (2014–2018) Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation. Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent. All the biases of ice concentration, ice cover, ice volume, and ice thickness can be reduced dramatically through ice concentration and thickness assimilation. The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system. The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation. Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-to-seasonal (S2S) Prediction Project, FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast. Since sea ice thickness in the PIOMAS is updated in time, it is a good choice for data assimilation to improve sea ice prediction skills in the near-real-time Arctic sea ice seasonal prediction. 相似文献